Application of competitive and transition petri layers in adaptive neuro-fuzzy controller

The article is a summary of previous work on the possibility of using Petri layers in adaptive neuro-fuzzy controllers. In the first part of the paper the controller and two types of Petri layer have been presented, competitive layer which resets certain signals and transition layer which causes omission of signals. Layer properties were described and comparison has been made. In the second part of the paper, the results of a simulation showing the advantages and disadvantages of proposed solutions have been presented. Both quality of reference signal tracking and energetic cost of control process have been calculated. In the last part, analysis and comments on the results were made. Main conclusions are that transition Petri layer can significantly reduce growth of numerical cost of the algorithm despite the increase of fuzzy rules count. Also both competitive Petri layer and transition Petri layer by changing some inner signals can affect output value of the fuzzy system and thus the control quality indicators change. Most positive solutions have been pointed ou

Analysis of neuro-fuzzy pid controller with Petri transition layer for DC motor working with ultra-low speed

Paper show analysis of possible usage of adaptive neuro-fuzzy controller with Petri transition layer as speed controller for drive system with DC motor with significant friction and working with ultra-low speed. Two identical DC motors are connected with stiff shaft, both friction and electrical parameters of power supply are included in model. Model has been build using Matlab SimPowerSystems library in order to include phenomenens which are significant in low and ultra low speed operation area. As low speed less than 10% of nominal speed is considered

Analysis of competitive petri layers impact on fuzzy Mamdani type regulator performance

The article analyzes the possibility of using competitive Petri layers in neuro-fuzzy controller for improvement of control quality. Principle of operation, and the possible implementation of a competitive Petri layer in controller are presented. A series of simulations and experiments are conducted in order to show improvement. As a research plant separately excited DC motor in a cascade control structure was used. Analyzed controller is used in the outer speed control loop

Analysis of adaptive neuro-fuzzy PD controller with competitive Petri layers in speed control system for DC motor

In the paper the issues related to the application of adaptive neuro-fuzzy controller for speed controller of an electrical motor are considered. Adaptive control structure with reference model (MRAS) is used. The standard controller is modified by the implementation of competitive Petri layers into its internal structure. The proposed modification improves the properties of the drive compared to the control structure with standard neuro-fuzzy controller. Theoretical considerations are confirmed by simulation studies experimental tests done on the laboratory stand

Application of the wavelet network to speed control of dc motor

This paper presents the possibility of using Wavelet network as DC motor speed controller in a cascade control structure. For this purpose cascade control structure has been modeled in MATLAB Simulink package. Possible to achieve dynamic has been tested during simulations. Methods allowing structure to remain stable under high trajectory has been proposed

Analysis of impact of initial weight vector values on work of the adaptive sensorless DTC-SVM control system

The paper presents the possibility of using neuro-fuzzy adaptive controller in sensorless direct torque control structure DTC-SVM of the induction motor. The influence of the initial set of weights parameters on the machine performance in case of incorrect identification of motor parameters has been investigated during simulations in MATLAB-SIMULINK package using SimPowerSystems toolbox

Neuro-fuzzy controller with petri transition layer in dc motor control system under ultra low speed trajectory – experimental verification

The main aim of this article is to verify experimentally possibilities of using neuro-fuzzy controllers to control the complex drive system in terms of low and ultra-low speed. Article is a complement and a summary of previous work. As the control object, the drive system with two DC motors was chosen. The drive motor is controlled using cascade control structure. The proposed neuro-fuzzy controller was used in the outer-speed control loop. Engines were connected using flexible coupling, to obtain two mass system. The study examined the adaptive PD and PID neuro-fuzzy controllers with and without transition layer, with and without recursion, with different number of membership functions. In the early chapters describe an adaptive neuro-fuzzy controller with a transition layer. It described the idea of transition layer and its position in the system controller. Later the analyzed controllers were described, the influence adaptation algorithm settings on the criteria of quality were examined. Finally experimental outcomes, that confirming the possibility of using speed control systems with adaptive neuro-fuzzy controllers with transition layer at ultra-low speed range have been presented. The final chapter contains a summary and conclusions

Sensorless induction motor drive system with adaptive controller and Petri layers

In the paper the adaptive control structure with induction motor drive system with MRAS type flux and speed estimator is tested and developed. System with the Petri layers was implemented and checked during different drive operations. Proposed algorithm was applied in the Direct Field Oriented Control Structure and Direct Torque Control of Induction Motor and tested in laboratory set-up with DS1202 dSpace Micro Lab Box card. Control structure was tested and checked during different drive operation

Analysis of usage of adaptive neuro fuzzy controller with competitive Petri layers in the control of DC motor

W artykule przedstawiono zagadnienia związane z zastosowaniem adaptacyjnej struktury sterowania z przestrajalnym regulatorem neuronowo - rozmytym, dla układu napędowego o nieznanym momencie bezwładności. Zastosowano adaptacyjną strukturę sterowania z modelem odniesienia. Regulator neuronowo - rozmyty zmodyfikowano poprzez wprowadzenie konkurencyjnej warstwy Petriego. Zmiana taka pozwoliła na poprawę właściwości dynamicznych układu napędowego z silnikiem prądu stałego w porównaniu do klasycznego regulatora neuronowo - rozmytego. Rozważania teoretyczne zostały potwierdzone przez badania symulacyjne wykonane w pakiecie SimPower system.The article presents the issues associated with the use of adaptive control structure with adaptive fuzzy controller for the drive system with unknown moment of inertia. Adaptive control structure with a reference model has been used. Competitive Petri layer was introduced to the fuzzy controller. This has allowed the improvement of the dynamic properties of the system as compared to the classic fuzzy controller. Theoretical considerations were confirmed by simulation

Adaptative vector control drive system with elastic joint

In the paper the analysis of vector controlled induction motor DRFOC elastic joint control system is described. The standard PI-controller in speed control loop has been replaced by the adaptive neurofuzzy controller wit 9, 25 and 49 rules, which allows complete adaptation to current work state. The impact of the number of rules on the quality of the adaptation was tested, while moment of inertia was changed